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Biomarkers are used to predict phenotypical properties before these features become apparent and, therefore, are valuable tools for both fundamental and applied research. Diagnostic biomarkers have been discovered in medicine many decades ago and are now commonly applied. While this is routine in the field of medicine, it is of surprise that in agriculture this approach has never been investigated. Up to now, the prediction of phenotypes in plants was based on growing plants and assaying the organs of interest in a time intensive process. For the first time, we demonstrate in this study the application of metabolomics to predict agronomic important phenotypes of a crop plant that was grown in different environments. Our procedure consists of established techniques to screen untargeted for a large amount of metabolites in parallel, in combination with machine learning methods. By using this combination of metabolomics and biomathematical tools metabolites were identified that can be used as biomarkers to improve the prediction of traits. The predictive metabolites can be selected and used subsequently to develop fast, targeted and low-cost diagnostic biomarker assays that can be implemented in breeding programs or quality assessment analysis. The identified metabolic biomarkers allow for the prediction of crop product quality. Furthermore, marker-assisted selection can benefit from the discovery of metabolic biomarkers when other molecular markers come to its limitation. The described marker selection method was developed for potato tubers, but is generally applicable to any crop and trait as it functions independently of genomic information.
The crucial role of carbohydrate in plant growth and morphogenesis is widely recognized. In this study, we describe the characterization of nana, a dwarf Arabidopsis (Arabidopsis thaliana) mutant impaired in carbohydrate metabolism. We show that the nana dwarf phenotype was accompanied by altered leaf morphology and a delayed flowering time. Our genetic and molecular data indicate that the mutation in nana is due to a transfer DNA insertion in the promoter region of a gene encoding a chloroplast-located aspartyl protease that alters its pattern of expression. Overexpression of the gene (oxNANA) phenocopies the mutation. Both nana and oxNANA display alterations in carbohydrate content, and the extent of these changes varies depending on growth light intensity. In particular, in low light, soluble sugar levels are lower and do not show the daily fluctuations observed in wild-type plants. Moreover, nana and oxNANA are defective in the expression of some genes implicated in sugar metabolism and photosynthetic light harvesting. Interestingly, some chloroplast-encoded genes as well as genes whose products seem to be involved in retrograde signaling appear to be down-regulated. These findings suggest that the NANA aspartic protease has an important regulatory function in chloroplasts that not only influences photosynthetic carbon metabolism but also plastid and nuclear gene expression.
Mass Accuracy Precursor Alignment is a fast and flexible method for comparative proteome analysis that allows the comparison of unprecedented numbers of shotgun proteomics analyses on a personal computer in a matter of hours. We compared 183 LC-MS analyses and more than 2 million MS/MS spectra and could define and separate the proteomic phenotypes of field grown tubers of 12 tetraploid cultivars of the crop plant Solanum tuberosum. Protein isoforms of patatin as well as other major gene families such as lipoxygenase and cysteine protease inhibitor that regulate tuber development were found to be the primary source of variability between the cultivars. This suggests that differentially expressed protein isoforms modulate genotype specific tuber development and the plant phenotype. We properly assigned the measured abundance of tryptic peptides to different protein isoforms that share extensive stretches of primary structure and thus inferred their abundance. Peptides unique to different protein isoforms were used to classify the remaining peptides assigned to the entire subset of isoforms based on a common abundance profile using multivariate statistical procedures. We identified nearly 4000,proteins which we used for quantitative functional annotation making this the most extensive study of the tuber proteome to date.
The essential mineral nutrient potassium (K(+)) is the most important inorganic cation for plants and is recognized as a limiting factor for crop yield and quality. Nonetheless, it is only partially understood how K(+) contributes to plant productivity. K(+) is used as a major active solute to maintain turgor and to drive irreversible and reversible changes in cell volume. K(+) also plays an important role in numerous metabolic processes, for example, by serving as an essential cofactor of enzymes. Here, we provide evidence for an additional, previously unrecognized role of K(+) in plant growth. By combining diverse experimental approaches with computational cell simulation, we show that K(+) circulating in the phloem serves as a decentralized energy storage that can be used to overcome local energy limitations. Posttranslational modification of the phloem-expressed Arabidopsis K(+) channel AKT2 taps this "potassium battery," which then efficiently assists the plasma membrane H(+)-ATPase in energizing the transmembrane phloem (re) loading processes.